Latest / The Adults In The AI Room / Dr. Mark Maybury on innovation and AI - Episode #20

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Dr. Mark Maybury on innovation and AI - Episode #20 · The Adults In The AI Room

• 0:22
  SPEAKER_00: Thanks so much for listening.
• 0:23
  SPEAKER_00: If this is your first time, we invite you to subscribe wherever you listen to podcasts.
• 0:28
  SPEAKER_00: And now here are your hosts, Seth Early and Chris Featherstone.
• 0:35
  SPEAKER_01: Welcome to today's podcast.
• 0:38
  SPEAKER_01: I'm Seth Early.
• 0:39
  SPEAKER_02: And I'm Chris Featherstone.
• 0:40
  SPEAKER_02: It's good to be with you.
• 0:42
  SPEAKER_01: How's your uh how's your fall going, Chris?
• 0:44
  SPEAKER_01: I know uh I've been thoroughly enjoying the weather here in uh Boston uh in Carlisle, and it's been spectacular.
• 0:52
  SPEAKER_01: I mentioned the last time we uh we did our recording that uh the the trees are just beautiful.
• 0:58
  SPEAKER_01: It's probably been the one of the best falls in recent memory.
• 1:01
  SPEAKER_02: Well, if if you look at the United States, right, you get this uh, it's almost like um, you know, take, I think from Northern California and just do a nice big, you know, moon style arc around the Pacific Northwest, and it's all in dark blue, meaning it's freezing.
• 1:20
  SPEAKER_02: And the rest of it is red, meaning it's super nice and warm.
• 1:25
  SPEAKER_02: And I'm in the cold part.
• 1:26
  SPEAKER_02: So we've got snow capped mountains already, and it snowed on me, it snowed on me yesterday a little bit.
• 1:33
  SPEAKER_02: So yeah, we're in the you know, in the throes of that, you know, that elevation of snow capped mountains, but beautiful changing trees, which nothing like what you guys have, but you know, because we've got tons of evergreens.
• 1:46
  SPEAKER_02: But anyway, yeah, man, we're here.
• 1:47
  SPEAKER_02: Winter's here.
• 1:49
  SPEAKER_01: Well, uh, let's jump into our podcast today.
• 1:52
  SPEAKER_01: So our guest today is a real renaissance man.
• 1:55
  SPEAKER_01: He's passing an experience over a broad array of interests from new business, new product development, innovation, digital transformation, AI and analytics, advanced manufacturing and commercialization.
• 2:06
  SPEAKER_01: He's just started a new advisory role at Ready Robotics, which will be helping him enable intuitive interfaces to cooperative robotics.
• 2:13
  SPEAKER_01: Please welcome Mark Mayberry.
• 2:16
  SPEAKER_04: Hey Seth, Chris, great to see you.
• 2:18
  SPEAKER_04: And uh, I'm actually not too far away from you in uh outside of Boston and Chelmsford.
• 2:22
  SPEAKER_04: And so I'm enjoying the beautiful fall weather as well.
• 2:25
  SPEAKER_01: Yeah, you're right down the street.
• 2:26
  SPEAKER_01: Yeah, isn't it gorgeous?
• 2:28
  SPEAKER_01: It's just great to have you, man.
• 2:29
  SPEAKER_03: We're excited to see you.
• 2:31
  SPEAKER_03: Beautiful, beautiful fall.
• 2:33
  SPEAKER_01: So so, Mark, uh, give us an update.
• 2:36
  SPEAKER_01: Uh, I know you uh it will give us give us your story, give us that arc of Mark, uh, Mark's arc and uh kind of how you got to where you are.
• 2:44
  SPEAKER_01: And I I know you've had some really interesting positions, but just give us kind of a rundown and the world according to you, how you got to where you are and what you're doing now, but uh give us the background, give us the lowdown.
• 2:55
  SPEAKER_02: I think, and and by the way, Mark, the you know, I I know I do, and I know um Seth does as well.
• 3:01
  SPEAKER_02: I love the human experience about all of this stuff too.
• 3:04
  SPEAKER_02: And you've got a unique one.
• 3:06
  SPEAKER_02: So, you know, feel free to start, you know, from you know, wherever is early, because I think part of this too is not only do we get you know those listeners that are super deep in AI and ML and very cerebral thinkers and you know, on the you know, whether it be in in academia, whether it be in commercial or whether it be in public sector, whatever, but they also want to know how folks like you got there, right?
• 3:31
  SPEAKER_02: And what the path was.
• 3:33
  SPEAKER_02: And I think that's pretty interesting.
• 3:35
  SPEAKER_04: Yeah.
• 3:36
  SPEAKER_04: Yeah.
• 3:36
  SPEAKER_04: So um uh I I've been very, very fortunate to have uh a rather blessed life.
• 3:41
  SPEAKER_04: And so uh, but you know, the ARC started uh obviously, like many of us, it starts at home, it starts with our parents and our family and friends.
• 3:50
  SPEAKER_04: And and our parents always, always emphasized education as being a doorway uh to the future.
• 3:56
  SPEAKER_04: And oh, it's interesting as a business person, uh successful executive years later, I look back and you know, we now have studies that show, you know, how RD actually generates wealth.
• 4:05
  SPEAKER_04: It generates, but of course, you got to translate that RD into commercial uh successes uh andor you know for our national security successes.
• 4:13
  SPEAKER_04: But my my arc started when I was 13.
• 4:15
  SPEAKER_04: I actually distinctly remember it.
• 4:17
  SPEAKER_04: I walked into uh an immersive environment, which at that time was a uh Adolby stereo um uh special effects future uh created uh uh uh by um uh by Star Wars, right?
• 4:33
  SPEAKER_04: And so uh I really you know had this incredibly immersive experience where um I had always loved math and science.
• 4:42
  SPEAKER_04: Um, but when I saw R2D2 and C3PO interacting with humans in a very natural way, cooperative way, um I kind of got real excited and I walked out and I distinctly remember saying, that is so cool, that's what I want to do with my life.
• 4:58
  SPEAKER_04: Um and so that started my my my love of artificial intelligence.
• 5:03
  SPEAKER_04: And it was, of course, for for sure, my environment was sparked by my dad, who was a computer executive in Cambridge, and you know, he worked up in self, you know, he's an army officer, then went went to Mitre and Raytheon, and then ultimately data architects and uh in and running an international company.
• 5:19
  SPEAKER_04: So, you know, he his his arc, you know, absolutely influenced us.
• 5:23
  SPEAKER_04: You know, I mean, literally he came down what came home one day because there was a flood in the basements of Cambridge, uh, and we had a PDP 11 in my my brother's bedroom.
• 5:31
  SPEAKER_04: And you know, when and we got to learn about, you know, people talk about Turing machines.
• 5:35
  SPEAKER_04: Well, we actually had had one in our bedroom and we were programming because we had the manual, you know, how do we turn this thing on?
• 5:41
  SPEAKER_04: How do we initialize it?
• 5:42
  SPEAKER_04: How do we, how do we, how do we create a what's a program, right?
• 5:45
  SPEAKER_04: And so uh absolute influence, but then you know, uh moved forward uh through college, uh, where I was an ROTC cadet, um, had a great experience of of literally flunking out my first uh semester, about two, two, about a month, month and a half into semester, I got called in the dean's office and it wasn't for an award.
• 6:04
  SPEAKER_04: And uh they uh you know for me and they looked at my the at my schedule and they said, What are you doing?
• 6:09
  SPEAKER_04: And I, you know, I thought I was in trouble.
• 6:10
  SPEAKER_04: I was like, Wait, well, I'm trying to be a good student.
• 6:12
  SPEAKER_04: They're like, You're taking all upper class courses as a freshman, except for, you know, except for uh reading and writing, which is required by all refreshmen, because I had AP'd out of all my courses, and I just was not prepared for college.
• 6:26
  SPEAKER_04: And so uh, you know, I'm taking organic chemistry, you know, semester one, you know, with with all the pre-med students, right, who had already been to college.
• 6:33
  SPEAKER_04: Uh, and so, you know, going from a public school to this private school, I ended up going from the bottom of that holy cross education to the top.
• 6:41
  SPEAKER_04: I graduated as valedictorian, graduated as cadet commander, uh, graduated, you know, uh as as a student who eventually went to uh to Cambridge, uh, got my master's degree uh under a Rotary Foundation scholarship.
• 6:56
  SPEAKER_04: I never expected even to finish college.
• 6:58
  SPEAKER_04: And I ended up getting not only my master's, but uh, but then my MBA and then my PhD.
• 7:03
  SPEAKER_04: Um, and that then led me into a career of national security.
• 7:08
  SPEAKER_04: And I went from intelligence to command and control, eventually transitioning uh after about 20 years to become the chief scientist of the Air Force, uh, the highest scientific position, a three-star equivalent position in the Pentagon.
• 7:20
  SPEAKER_04: Um, and uh, and then uh after that came back to MITRE, uh uh man uh which manages seven federal laboratories.
• 7:28
  SPEAKER_04: I was the chief technology officer, chief security officer.
• 7:31
  SPEAKER_04: And then I got a call, um, as often does happen.
• 7:35
  SPEAKER_04: Uh, you know, you don't apply to some of these jobs.
• 7:37
  SPEAKER_04: I get a call, and it was, you know, a friend of a friend, a board member who knew a board member who I who I'd worked with, um, recommended me.
• 7:45
  SPEAKER_04: And I ended up as the first chief technology officer of Stanley Black and Decker.
• 7:49
  SPEAKER_04: And so that's kind of my arc.
• 7:51
  SPEAKER_04: Um, I actually just just retired from Stanley Black and Decker.
• 7:55
  SPEAKER_04: I'm about to start a new uh executive position.
• 7:58
  SPEAKER_04: It's not disclosable yet, but it'll be at a very large defense contractor.
• 8:02
  SPEAKER_04: Um exciting role.
• 8:04
  SPEAKER_04: I also thank you very much.
• 8:06
  SPEAKER_04: I also set on four boards and Ready Robotics is is uh I'm an advisor uh to them.
• 8:10
  SPEAKER_04: I'm also an advisor to a uh company called Floor Coatings, which is a nanotechnology uh uh corporation, also an advisor to a company that's a uh a very innovative uh energy uh company, wind energy company, which is called Halo.energy, which actually takes uh the turbines uh that you see traditionally on the wind towers and it puts a fourth one around it.
• 8:32
  SPEAKER_04: Very, very clever design.
• 8:33
  SPEAKER_04: The the you know, in if for those of you listeners who are physicists who know the Bernoulli effect or aerospace scientists, they realize that actually that enhances the Bernoulli effect and it actually makes them 50% more efficient.
• 8:44
  SPEAKER_04: So um now it doesn't scale, so there's some you know challenges with any technology, but it works really, really well on small applications like remote applications.
• 8:53
  SPEAKER_04: Um, and so and and I also I'm also the the the uh the chair of the nominations and and uh and uh governance committee of a of a company that's that's uh uh planning to go public uh called Internet Sciences, which focuses on IoT and cybersecurity.
• 9:08
  SPEAKER_04: So I'm I'm quite involved.
• 9:10
  SPEAKER_01: You're a busy man.
• 9:11
  SPEAKER_04: I'm a busy man.
• 9:12
  SPEAKER_01: And in your spare time, you wonder why you have no spare time.
• 9:16
  SPEAKER_04: Well, actually, I I do, and this is this is advice to your listeners is all of my spare time uh is actually planned in the sense that um uh I don't sort of you know wake up on a Saturday morning and say, Oh, what am I gonna do?
• 9:30
  SPEAKER_04: You know, I got a whole weekend free to everything.
• 9:31
  SPEAKER_04: Some people like that, right?
• 9:32
  SPEAKER_04: I don't.
• 9:33
  SPEAKER_04: I I like you know, tomorrow tomorrow morning, uh, you know, we're we're on a Friday today and and Saturday morning.
• 9:38
  SPEAKER_04: I'll be up at 6:30, 7 o'clock uh to drive into Boston, pick up my daughter, and then bring her and I, her and I coach a young women's ice hockey team that's nationally bound, nationally, number two in the nation right now, statistically.
• 9:50
  SPEAKER_04: Uh our intent is to get to number one.
• 9:52
  SPEAKER_04: We'll see, we'll see how we go do.
• 9:54
  SPEAKER_04: But but you know, I do believe a full life uh where you plan engagements with your children, your grandchildren, your colleagues, your your personal joys, whatever you get joy out of.
• 10:05
  SPEAKER_04: Uh, you know, to me, for me, that energizes me.
• 10:08
  SPEAKER_04: It brings me brings me new renovation.
• 10:10
  SPEAKER_04: That's awesome.
• 10:11
  SPEAKER_02: So a little bit going on.
• 10:13
  SPEAKER_02: So ice hockey, um, oh, we could.
• 10:16
  SPEAKER_02: I'm not a big ice hockey player, but I'll tell you what, I have a uh huge respect for those that get out on the ice and can do what they do.
• 10:24
  SPEAKER_02: Um, I look like I have an inner ear problem if I get on ice skates, right?
• 10:28
  SPEAKER_02: So they don't knew that one for me, but but that's phenomenal, man.
• 10:32
  SPEAKER_02: Congrats!
• 10:33
  SPEAKER_04: Yeah, it's a it's a it's a wonderful sport.
• 10:35
  SPEAKER_04: I I I believe very much in team sports, I believe in high speed sports that are cardiovascular, you know, whether it's soccer, basketball, whatever.
• 10:43
  SPEAKER_04: Those are all great sports.
• 10:45
  SPEAKER_04: They teach kids uh not only individual discipline.
• 10:48
  SPEAKER_04: Like, I mean, our girls are 12 and 13 years old, and we're teaching them what they want to put in their bodies and how it's gonna affect them.
• 10:55
  SPEAKER_04: We teach them how the training as individuals and as teams makes them more successful.
• 11:00
  SPEAKER_04: And, you know, it there's so many life lessons learned through this experience.
• 11:04
  SPEAKER_04: I've seen my own children, uh, all of who skate, uh, one of who skate at the collegiate level, several, several of two of them who have been who are ice hockey coaches together with me, and all of them have learned to love the sport because it's you know, it's emotional, it's physical, it's strategic if you know what you're doing, but it's also tactical.
• 11:23
  SPEAKER_04: It's it's a it's a great life, life experience.
• 11:26
  SPEAKER_02: I've been I've been I haven't pulled all the data points, but I'm slowly but surely trying to pull the data points of those who love dynamic sports and classical music, but also have an affinity towards hard rock music and then other intense activities, right?
• 11:40
  SPEAKER_02: And there's this, and also our our um we won't be declared mathematicians, but love, you know, those types of cerebral activities of understanding and solving problems, right?
• 11:52
  SPEAKER_02: So it's it's this is a gamut of like um, whether it be like you're saying, you know, with with certain with uh with hockey or basketball or soccer or you know, something where you don't have total control, but there's the dynamic element and being able to set things up and execute and stuff in those scenarios.
• 12:13
  SPEAKER_02: There's there is an association.
• 12:15
  SPEAKER_02: I just haven't found all of the data points yet, but I'm looking.
• 12:18
  SPEAKER_04: Well, Chris, there's there's some fascinating um I'm I'm uh blessed to have a a daughter who uh is a graduate student in neuroscience.
• 12:27
  SPEAKER_04: And so you can imagine, you know, we got computer scientists in our family, neuroscientists, right?
• 12:31
  SPEAKER_04: It's quite interesting.
• 12:32
  SPEAKER_04: Uh and and we actually know there is some data now about the brain on uh on music and the brain on sports.
• 12:40
  SPEAKER_04: And uh it turns out actually, uh everything from memory in your hippocampus to emotion in your amygdala to your prefrontal cortex that's focusing on planning and scheduling.
• 12:51
  SPEAKER_04: Um, all of those things, whether it's music, where people think, oh, you know, I'm actually a musician, I'm a I'm a I'm a professionally trained percussionist.
• 12:59
  SPEAKER_04: I also play guitar, um, not that well, but I played percussion really well.
• 13:04
  SPEAKER_04: And and that goes through the from over a decade of discipline training with somebody who played you, you mentioned classical music.
• 13:11
  SPEAKER_04: You played in the Boston Symphony Orchestra, although I was actually a general business, a rock and a pop music uh yeah, and also jazz.
• 13:19
  SPEAKER_04: I I my personal affiliation is with jazz, and it turns out jazz, one of the higher forms, I believe, of music is you have to understand all the basics.
• 13:29
  SPEAKER_04: So if you play classical, you can read music, you understand the mathematics behind it, you understand scales, the structure, transpositions.
• 13:36
  SPEAKER_04: If you're talking to bringing things from one scale to another scale, you actually learn all of those skills, just like in hockey.
• 13:42
  SPEAKER_04: You learn all those basics about edge control and puck control, and then you build on top of those, you scaffold on top of those rhythm and tonality, right?
• 13:51
  SPEAKER_04: And structure and call and take, give gives, give and takes.
• 13:54
  SPEAKER_04: And those are can be in physical space, those can be in time, right?
• 13:58
  SPEAKER_04: And so you all of these patterns turn out to be, as you as you alluded to, consistent across these disciplines.
• 14:06
  SPEAKER_04: And that's why we always tell people don't be a single sport person, you know, cross-train.
• 14:12
  SPEAKER_04: Uh, and similarly, with your mind, don't be a single discipline person.
• 14:16
  SPEAKER_04: Cross-train, love the arts, love books, love people, you know, and and love science, and and you'll be a more interesting, richer person for it.
• 14:24
  SPEAKER_01: That's wonderful.
• 14:25
  SPEAKER_01: What else do you uh what else do you play in terms of sports?
• 14:27
  SPEAKER_01: Or do you have other uh other things that you like to do?
• 14:30
  SPEAKER_01: Oh, yeah.
• 14:30
  SPEAKER_04: Yeah, well, I I played uh varsity tennis when I was in in in high school.
• 14:34
  SPEAKER_04: I didn't I didn't transition, I played a little bit in college, but I still that's a life sport.
• 14:38
  SPEAKER_04: I also like to encourage people to play sports.
• 14:41
  SPEAKER_04: You know, I'm in good physical shape.
• 14:43
  SPEAKER_04: Why?
• 14:43
  SPEAKER_04: Because I ice skated with my two kids on Wednesday night and on Tuesday night and last night, Thursday night, I skated with my girls, right?
• 14:50
  SPEAKER_04: So uh so the point there is that um, you know, and I run my knee, it running hurts a little bit in my knees.
• 14:56
  SPEAKER_01: So sometimes um, you know, but I but I swim.
• 14:59
  SPEAKER_04: I swam varsity uh as well in in in college.
• 15:02
  SPEAKER_04: And so, you know, I go to a hotel and I'm like, what do you what do you got?
• 15:06
  SPEAKER_04: You know, for you know, because you know, after dinner or before dinner, um, I want to, you know, and here's another you know, piece of wisdom I've learned from the health science folks is that you know, we oftentimes think um, you know, I've got to put in an hour every day of training and so on.
• 15:22
  SPEAKER_04: Actually, we're learning now, not true.
• 15:24
  SPEAKER_04: And even in walking, I walk three times a day with my wife.
• 15:28
  SPEAKER_04: Um, and uh I we now know that actually maybe six or eight thousand steps is enough.
• 15:33
  SPEAKER_04: You don't need to do 12,000 steps.
• 15:35
  SPEAKER_04: I I probably do 20 to 30,000 a day, you know.
• 15:38
  SPEAKER_04: So I'm overworking my body, and so you know, I I'm a I'm I'm a lazy mathematician, and so I always like to tell people, you know, do the minimalist for the maximum.
• 15:48
  SPEAKER_04: And so if you get, you know, if you're just moving a little bit, you're getting 80% of the value, you know, get moving.
• 15:54
  SPEAKER_01: And then learning new things.
• 15:56
  SPEAKER_01: I just my wife and I recently uh started uh Aikido, which is oh great, very different.
• 16:02
  SPEAKER_01: I've done other martial arts on Shotokan over the years, but decades.
• 16:06
  SPEAKER_01: But Aikido is so interesting, and it's it's it's training your brain to do something completely different, and it's a lot of kinesthetic stuff.
• 16:13
  SPEAKER_01: So that's been a lot of fun for us.
• 16:14
  SPEAKER_01: Hey, I want to jump back to um your transition when you went to uh Black and Decker.
• 16:20
  SPEAKER_01: What was that like?
• 16:20
  SPEAKER_01: I mean, you went from being a chief scientist, you went from doing all this really you know interesting leading edge stuff to an organization that well, tell us what what was what was it like and what was that transition like?
• 16:33
  SPEAKER_01: And what kinds of things do they bring you into a call?
• 16:36
  SPEAKER_04: Well, great question, Seth.
• 16:37
  SPEAKER_04: You know, and and by the way, just final point on you and your wife Leonardo, doing things with others, whether it's with your kids, your spouse, your friends, whatever, that actually we know correlates with your stick to itness.
• 16:51
  SPEAKER_04: That's right.
• 16:51
  SPEAKER_01: That's your likely social.
• 16:53
  SPEAKER_01: It becomes social.
• 16:54
  SPEAKER_01: Yeah, that's because you is.
• 16:56
  SPEAKER_01: I go see my friends, yeah.
• 16:57
  SPEAKER_01: I also see my motivation.
• 16:59
  SPEAKER_04: Yeah, exactly exactly.
• 17:00
  SPEAKER_04: You know, your wife says, You say, Oh, I'm not feeling tennis.
• 17:02
  SPEAKER_04: Well, what's wrong?
• 17:03
  SPEAKER_04: You got in pain?
• 17:03
  SPEAKER_04: It's like, no, I just says, get your butt up, let's get going.
• 17:07
  SPEAKER_04: And before you know it, five minutes into it, you're like, Hey, actually, I feel like you don't even think about it.
• 17:11
  SPEAKER_01: That's right.
• 17:12
  SPEAKER_01: Yeah, yeah, it's a really great point.
• 17:14
  SPEAKER_04: So, so back to your your your question, Seth.
• 17:17
  SPEAKER_04: Um, it you know, some respects it was exactly like other things I've experienced, by other things being executive transitions.
• 17:24
  SPEAKER_04: And in other cases, it was it was completely different than what I expected.
• 17:28
  SPEAKER_04: So, some of the similarities.
• 17:30
  SPEAKER_04: Um, it was, you know, a big organization, right?
• 17:33
  SPEAKER_04: We it started with 11 billion.
• 17:35
  SPEAKER_04: When I left, we were at 17 billion.
• 17:37
  SPEAKER_04: So four and a half years of growing that organization.
• 17:40
  SPEAKER_04: Uh, that was exciting.
• 17:41
  SPEAKER_04: Well, I had worked in the Air Force.
• 17:43
  SPEAKER_04: The Air Force is a$110 billion organization.
• 17:45
  SPEAKER_04: If it were a for-profit, it would be considered a fortune five, five, you know, for 100, maybe fortune 50.
• 17:52
  SPEAKER_04: So uh, you know, so I was used to working with bigger organizations.
• 17:55
  SPEAKER_04: And some people like big organizations, some hate them.
• 17:58
  SPEAKER_04: I love both.
• 17:58
  SPEAKER_04: I love the small um because you know, you're at the atomic level, you can influence shape, and that's why I work with startups.
• 18:05
  SPEAKER_04: But I particularly like the big because the bigs, if they can be retrained, you can make the elephant dance.
• 18:11
  SPEAKER_04: And that can be quite powerful if you get them dancing in the right direction and not crushing things.
• 18:17
  SPEAKER_01: Um exactly, exactly.
• 18:22
  SPEAKER_04: So, so you know, Stanley Black and Decker obviously going from nonprofit to profit.
• 18:27
  SPEAKER_04: That's a big shift.
• 18:29
  SPEAKER_04: Um, and so so you know, thinking with, you know, how do I think about margin?
• 18:34
  SPEAKER_04: You know, how much profit I can make on something, but equally, how can I manufacture something that's scale?
• 18:39
  SPEAKER_04: Uh, and an equal challenge, a global company.
• 18:42
  SPEAKER_04: I was working in the Air Force was a global organization, but MITRE was a national organization.
• 18:46
  SPEAKER_04: It had a global footprint, uh, but but principally it was a US organization.
• 18:50
  SPEAKER_04: So, frankly, uh, you got to be an American to work there.
• 18:52
  SPEAKER_04: Well, all of a sudden, you're thrust into a company where you know, Americ being an American could be valuable, but maybe not so much if you're uh running a plant in Israel or China or even Russia.
• 19:03
  SPEAKER_04: We we divested our Russian assets because of what's going on in the world, uh, but we we we're operating in Russia for a while.
• 19:10
  SPEAKER_04: And that that, you know, how how do you account?
• 19:12
  SPEAKER_04: How do you make sure that you're living in a kleptocracy that that business still succeeds for your shareholders?
• 19:18
  SPEAKER_04: You know, not an easy challenge.
• 19:20
  SPEAKER_04: And so so the multiculturalism was a massive change.
• 19:24
  SPEAKER_04: Um, the the the uh the the manufacturing uh focus, uh my company, previous company in MITRE was by law, right, by statute, disallowed for manufacturing, right?
• 19:36
  SPEAKER_04: You can create stuff, but you cannot manufacture.
• 19:39
  SPEAKER_04: That's what industry does.
• 19:40
  SPEAKER_04: So now I went to industry.
• 19:41
  SPEAKER_04: And guess what?
• 19:42
  SPEAKER_04: Well, industry is manufacturing.
• 19:44
  SPEAKER_04: So one of the benefits is I have all of that knowledge of the RD space, of all the national laboratories, and I even use them in my job.
• 19:52
  SPEAKER_04: It's in the Blackendecker to invent the product that was a COVID deactivation product.
• 19:59
  SPEAKER_04: And that um what was enabled by realizing these different organizations have different strengths and weaknesses.
• 20:05
  SPEAKER_04: And you know, you're not going to get the kind of you know, forward-looking, high-risk research that the federal government can afford to invest it, that maybe a for-profit can't.
• 20:14
  SPEAKER_04: But it was exciting, it was a wonderful ride, great company, great set of people, um, really interesting digital design and 3D printing and and digital uh e-commerce.
• 20:24
  SPEAKER_04: We we grew the e-commerce to two and a half billion dollars, you know.
• 20:28
  SPEAKER_04: But the I managed the most exciting last point I make is the most exciting thing about it was I got to manage all the RD across a heterogeneous business, a business that makes things that you don't even know about.
• 20:41
  SPEAKER_04: Like nine out of the 10 cars in your in your garage or in the parking lot are held together by robotically inserted fasteners that Stanley Fast Engineer Fasteners makes.
• 20:52
  SPEAKER_04: And it doesn't make a difference if you're in Japan, China, US, we Europe, all of those are robotically inserted fasteners.
• 21:01
  SPEAKER_04: That's what we're the best in the world.
• 21:02
  SPEAKER_04: Make a billion of them a year.
• 21:04
  SPEAKER_04: So you wouldn't know that you you would you wouldn't know that electronic doors, all these kinds of things.
• 21:09
  SPEAKER_04: So it was a it was a great experience.
• 21:11
  SPEAKER_02: I'll I'll tell you for a time period, I um managed an RD where we actually made physical assets and stuff.
• 21:18
  SPEAKER_02: And I don't think people can grasp if you're in software, you're in software.
• 21:22
  SPEAKER_02: If you're in manufacturing, you're in manufacturing, but I don't think they can grasp the concepts too now, or it we now we we are, but what that looks like of bringing together electrical engineers with software engineers, with embedded engineers, with process engineers, with you know, mechanical engineers, and dovetailing all of these processes together to release a product, right?
• 21:45
  SPEAKER_02: Because now, you know, before it was like hey, my electrical, my in my um mechanical and my process guys, they're out building a product and hopefully they're doing life accelerated testing and they're doing all that kind of stuff so we release a product with quality, and then I have my Software guys who may be doing some embedded work on that same thing, but they didn't have to like dovetail now with you know IoT and all of the the you know telemetry and instrumentation, all of that system is coming off the hardware is now populating, whether it be robotics in the manufacturer, whether it be the actual product, is now populating up to the cloud to actually do something with and make insights, whether it be proactive or preventative maintenance, or whether that just be data on what is happening, right?
• 22:32
  SPEAKER_02: So I'd love to get a take because I'm I I can only assume the amount of data produced by Stanley Blackendecker and what you did all with all of it and what that helped you with, right, going forward.
• 22:43
  SPEAKER_02: Yeah.
• 22:44
  SPEAKER_04: So so you know, this you know, Chris, this is a uh it's a great point.
• 22:47
  SPEAKER_04: Uh, you know, we're absolutely in this transformation where we're going from a completely analog physical world to one where it's mixed reality, if you will, right?
• 22:55
  SPEAKER_04: So we get physical and we got digital.
• 22:57
  SPEAKER_04: Um, I'm I'm really uh fortunate right now to I'm leading a congressionally directed study for the Defense Science Board that's focused on digital engineering.
• 23:05
  SPEAKER_04: So I'm I'm actually spending spending my days um in between my assignments because I'm about to step into a new executive role, um, uh actually running a national study um uh where uh we're work looking at both in the commercial sector, but also in the government sector, because you know, we play, we we we make aircraft, we make spacecraft, we make watercraft, sub, you know, submarines, et cetera.
• 23:29
  SPEAKER_04: Um so all of those are very, very sophisticated systems, but equally in the commercial world, whether it's an aircraft manufacturer, a car manufacturer, a product manufacturer, tools, um, uh uh andor outdoor, right?
• 23:41
  SPEAKER_04: All of these things uh in the best in the leading edge companies and or government organizations are born digital.
• 23:48
  SPEAKER_04: They're designed digitally, but to your point, they have to be physically manufactured, right?
• 23:54
  SPEAKER_04: And so whether you're, you know, at Stanley Black and Decker uh to answer your question, we digitally design all of our tools and then we 3D print them.
• 24:02
  SPEAKER_04: Uh and because we want to look at ergonomics, we want to share them with uh our channel partners, you know, the lows and home depots of the of the world and say, hey, do you think your customers will like these or bring them to the pros and say, what do you think about this?
• 24:13
  SPEAKER_04: They say, Oh, that's terrible, right?
• 24:14
  SPEAKER_04: You know, I I you know, this other one, I love, right?
• 24:17
  SPEAKER_04: Okay, let's let's manufacture to that spec.
• 24:20
  SPEAKER_04: And so the that that that physic digital and physical integration, and as you point out, the mechanical, electrical, structural, all of that, right?
• 24:28
  SPEAKER_04: Um, uh it and also interestingly with materials, because nowadays we actually have materials that can interact, right?
• 24:35
  SPEAKER_04: And you can have smart materials, right?
• 24:37
  SPEAKER_04: Meta materials that can that can react to you know charges uh and or um heat, uh pressure, right?
• 24:44
  SPEAKER_04: And so now all of a sudden, and we even you were talking hockey before, you know, my hockey skates are foreign to my feet, right?
• 24:50
  SPEAKER_04: Uh via heat and pressure.
• 24:51
  SPEAKER_04: And so, so even in sports, I have Kevlar in the bottom of my hockey skates, right?
• 24:55
  SPEAKER_04: You know, and that that stuff was made for the military, but it's got relevance uh in terms of protecting my bones and my soft tissue.
• 25:02
  SPEAKER_04: And so, so uh it's interesting how both in the commercial and in the government world, um, that that the this digital transformation is enabling a transformation, by the way, not only of the product design, but also the manufacturer.
• 25:17
  SPEAKER_04: Because one of the things we were doing at Stanley Black and Decker, really exciting, is we had the factory of the future, what we call the manufacturing.
• 25:24
  SPEAKER_04: And that factory was uh was an aggregation across all the different disciplines from industrial to tools to outdoor, all of those in security, all of those needs were brought into what should the factory of the future include?
• 25:38
  SPEAKER_04: And part of that was having modular robot robots, uh, you know, so you'd have mobile robots so that people wouldn't have to move stuff around plants, but you also can have mobile robots that are programmable, whether they be caged or free range, right?
• 25:53
  SPEAKER_04: And and you can take those digital designs for those products and pour them into um both the robots, you know, in terms of what they should do in terms of cutting or or or drilling or screwing or labeling, right?
• 26:05
  SPEAKER_04: So you can use all that, um, or even use it to redesign the factory so that your factory is optimized um for this.
• 26:13
  SPEAKER_04: And then ultimately those digital designs can be used for marketing, right?
• 26:16
  SPEAKER_04: You can use them for uh displaying your product, flying through your product, uh showing it in operation, all of those kinds of things.
• 26:24
  SPEAKER_04: So you're absolutely right.
• 26:25
  SPEAKER_04: Digital is uh is is critical.
• 26:28
  SPEAKER_04: And one of the things I do, for example, in Ready Robotics, um, which is a cool company because, and I'm an advisor to it because they have they have flipped from physical robots, making them manipulable by humans and and cooperative with humans, to actually common operating systems that make every robot look the same to the humans uh via an intuitive interface.
• 26:49
  SPEAKER_04: And so what they're doing is saying, hey, the human has to be centered here by making the robot much more adaptable and programmable.
• 26:58
  SPEAKER_01: We'll be right back.
• 27:00
  SPEAKER_01: We'd like to thank Marketing Artificial Intelligence Institute for their support of this podcast.
• 27:05
  SPEAKER_01: Marketing AI Institute is a media, event, and online education business that makes AI approachable, actionable, and accessible to marketing leaders around the world.
• 27:14
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• 27:25
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• 27:32
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• 28:01
  SPEAKER_02: Their information architecture expertise has allowed clients from all over the world to efficiently organize, access, and utilize their data, ultimately resulting in better business outcomes.
• 28:10
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• 28:29
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• 28:33
  SPEAKER_02: Working with customers in AI, the number one question that I get usually is where do I start?
• 28:38
  SPEAKER_02: What data do I need?
• 28:39
  SPEAKER_02: How do I gather it?
• 28:40
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• 28:42
  SPEAKER_02: Well, if you want to catch the wave of AI, not be crushed by it, you really do need to read a book written by Seth Early called The AI Powered Enterprise.
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• 29:37
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• 29:43
  SPEAKER_01: Who uh I could call you a data strategist.
• 29:46
  SPEAKER_01: I could call you a number of different things.
• 29:48
  SPEAKER_01: What do you like to what do you like your title to be these days?
• 29:50
  SPEAKER_01: What do you you're an advisor?
• 29:52
  SPEAKER_01: You're you've talked about many, many different things.
• 29:54
  SPEAKER_01: You're a scientist, you're you're an engineer, you're you know what how do you what's your money, what's your labeling?
• 29:59
  SPEAKER_01: I'm big um big on labeling.
• 30:01
  SPEAKER_04: Well, either either grandpa or coach.
• 30:04
  SPEAKER_04: Those are my things.
• 30:05
  SPEAKER_04: There you go.
• 30:06
  SPEAKER_04: That's it.
• 30:07
  SPEAKER_01: How many grandkids do you have?
• 30:08
  SPEAKER_04: How many grandkids?
• 30:10
  SPEAKER_04: We have two who are with us, and we have one that's forthcoming.
• 30:13
  SPEAKER_01: Oh, congratulations.
• 30:15
  SPEAKER_01: Congratulations.
• 30:16
  SPEAKER_01: I've I've been 12, uh, 14 and uh 10 months or uh so get to rent over.
• 30:23
  SPEAKER_01: Yeah, hey.
• 30:26
  SPEAKER_01: Yeah, I have a quick question about um you got your PhD.
• 30:30
  SPEAKER_01: Uh was uh early uh in in the late 90s, correct?
• 30:34
  SPEAKER_04: Or early I I I I did my master's from 86 to 87 at Cambridge in computer speech and language, and then I did my doctorate uh up until nine September of 90.
• 30:44
  SPEAKER_04: Uh it was actually awarded in 91.
• 30:46
  SPEAKER_04: Uh, and and uh in uh in and I basically I create invented the first computer program to write multi-paragraph and and multilingual texts.
• 30:58
  SPEAKER_04: So it could it could essentially essentially I created an author, a digital author.
• 31:03
  SPEAKER_01: And but what was it like back then?
• 31:04
  SPEAKER_01: Because that was still kind of that early stages, maybe a little bit of the AI winter, would you say?
• 31:10
  SPEAKER_01: Was it or was it starting to re-emerge?
• 31:14
  SPEAKER_04: I I think I think unlike today where AI is uber all us, it's kind of everywhere, right?
• 31:18
  SPEAKER_04: And everybody wants to work in AI.
• 31:20
  SPEAKER_04: Um, uh it was it was very unique.
• 31:22
  SPEAKER_04: And I can tell you when I graduated, I was one of four, four um Air Force officers who had some kind of degree, not all a PhD, but some kind of a degree, some masters in AI.
• 31:36
  SPEAKER_04: So it was a very, very select club.
• 31:38
  SPEAKER_04: And I and actually it was kind of nice because I was very fortunate.
• 31:42
  SPEAKER_04: People before me, uh, you know, uh Colonel Steve Cross at the time, North Fowler, these are people in the Air Force, and in Doug White, Air Force Research Laboratory, they all had AI backgrounds and were investing in AI.
• 31:55
  SPEAKER_04: And so at Cambridge, it was a is a as a rather um, I want to say I don't say lonely experience because we had I had 10 other people.
• 32:03
  SPEAKER_04: That's how small my class was.
• 32:04
  SPEAKER_04: 10 people who are computational linguists and speech scientists.
• 32:09
  SPEAKER_04: So if we think today how big Alexa is or Siri, right?
• 32:12
  SPEAKER_04: Everybody's got it in their pocket, right?
• 32:14
  SPEAKER_04: They're running around speaking to their little agents, they're in their car, you know, uh and that was the foundational work back then, right?
• 32:20
  SPEAKER_04: It was we had we had the only at Cambridge, the only 100,000-word speaker independent uh um uh 1,000-word vocabulary uh speaker independent system in the world.
• 32:32
  SPEAKER_04: Um the only the only other quick one that came closest was at ATR in Japan.
• 32:35
  SPEAKER_04: And so, you know, I literally had a bank of VAC stations and you know, uh just you know, state of the art digital had just made this major investment.
• 32:44
  SPEAKER_04: And then when I left Cambridge, I went to the Air Force and they said, okay, we want you to run our AI lab.
• 32:49
  SPEAKER_04: And I had uh uh a half a dozen list machines, and so I had I had like hundred, two hundred thousand dollars at the time, a lot of money, or you probably say like a million dollars today, you know, of for each machine.
• 33:01
  SPEAKER_04: And they had object-oriented operating systems, object uh-oriented simulations, knowledge-based uh simulations.
• 33:08
  SPEAKER_04: So things we don't even have today.
• 33:10
  SPEAKER_04: Today we don't have object-oriented operating systems.
• 33:13
  SPEAKER_04: We had those uh to do so, so I was very, very, very fortunate, but it was also a very um, you know, like most PhD experiences, it's a very solitary experience because you're the only one in the world who knows what you're working on and you're the world's expert, you're becoming the world's expert.
• 33:30
  SPEAKER_01: Right, right, right, right.
• 33:31
  SPEAKER_01: That's so interesting.
• 33:32
  SPEAKER_02: What what how did you guys base your models?
• 33:34
  SPEAKER_02: Because we didn't even have neural nets back then, right?
• 33:37
  SPEAKER_02: So it was was it more concatenative and and you know, search patterns and things?
• 33:41
  SPEAKER_02: How did how'd you guys do it?
• 33:43
  SPEAKER_02: Great question.
• 33:45
  SPEAKER_04: So so we actually did have neural networks.
• 33:47
  SPEAKER_04: Neural networks go back to the 50s, you know, in and 60s or you know, Minsky and others working as perceptrons and so on.
• 33:53
  SPEAKER_04: So it's not that we didn't have the concepts, but you're right, we didn't have the AWS's and other cloud services, yeah.
• 34:01
  SPEAKER_04: You know, we didn't have you know the uh GPT-3, right?
• 34:04
  SPEAKER_04: There were no machine learning uh systems for us to kind of plug in.
• 34:09
  SPEAKER_04: So guess what?
• 34:10
  SPEAKER_04: You become the the the the the the learning the machine learning system.
• 34:14
  SPEAKER_04: So literally, and by the way, what's what's more difficult is not that we didn't have the computing and that we didn't have you know the models and literature, no one understood the methodology.
• 34:24
  SPEAKER_04: So it wasn't like you went in and as a freshman, they said, We're gonna teach you about machine learning.
• 34:30
  SPEAKER_04: No, no, no, no.
• 34:31
  SPEAKER_04: There was no machine learning field, there was no journal of machine learning, there was no community of machine learning.
• 34:36
  SPEAKER_03: You were alone, right?
• 34:37
  SPEAKER_04: And so I went in, and interestingly, I had a an undergraduate experience when I was the, I told you I almost flunked out of college, right?
• 34:46
  SPEAKER_04: I ended up becoming the Femric scholar.
• 34:48
  SPEAKER_04: They pick one a year, they actually picked two.
• 34:50
  SPEAKER_04: There's a friend of mine, uh Mark Nevins, who worked on Beowulf, and I worked on AI because I wanted to work in AI.
• 34:56
  SPEAKER_04: And what did I do?
• 34:57
  SPEAKER_04: I worked with neuropsychologists and we built an expert system to diagnose neuropsychology.
• 35:03
  SPEAKER_04: And how do we do it?
• 35:04
  SPEAKER_04: Well, I asked the humans, I said, How do you do this, guys?
• 35:07
  SPEAKER_04: And gals.
• 35:08
  SPEAKER_04: And the doctors, there was a set of doctors at Holy Cross and at Boston University, and they said, We have an image of a Christmas tree.
• 35:17
  SPEAKER_04: It's the brain like a Christmas tree, and we look at all these reports and little lights light up.
• 35:22
  SPEAKER_04: Some of them are bright, some of them are dim.
• 35:24
  SPEAKER_04: But uh, but after all these things light up, we say, Oh, the person has an occipital lobe, which is in the back of your brain.
• 35:30
  SPEAKER_04: That's where all the reports tell us is a problem.
• 35:32
  SPEAKER_04: So they had a visual model.
• 35:34
  SPEAKER_04: So, what did I do?
• 35:35
  SPEAKER_04: I built a Bayesian net.
• 35:36
  SPEAKER_04: I built a Bayesian because I looked around at the models, I talked to mathematicians.
• 35:40
  SPEAKER_04: They said, it's it's a probabilistic, you know, Bayesian kind of thing.
• 35:45
  SPEAKER_04: And so build a Bayesian model.
• 35:46
  SPEAKER_04: So I built a a no one I invented this, I probably should have gotten a patent on it, but I built a frame-based system which had probabilities associated with each of the frame elements and essentially created, we didn't have the high high-tech visualization at the time, but in your mind, you could see statistically what that what that Christmas tree looked like.
• 36:03
  SPEAKER_04: So when I got to Cambridge and they said, we want you to build uh, well, I actually proposed, I said, no one has ever done uh uh written a computer program that can author.
• 36:14
  SPEAKER_04: You know, what would that be like?
• 36:16
  SPEAKER_04: And so what I did is I went out to all of the English professors and I asked them, what textbooks do you use?
• 36:23
  SPEAKER_04: How do you teach your freshmen and your graduate students to write like Faulkner or write like you know, words with a word, whatever.
• 36:30
  SPEAKER_04: And and they they they said, these are the methods.
• 36:33
  SPEAKER_04: And what's shocking about this is I learned how to write a description, a comparison, exposition, argument.
• 36:42
  SPEAKER_04: Those are the four major forms.
• 36:43
  SPEAKER_04: And if you read my PhD thesis, I actually write exactly how you write those things and I tie them to knowledge structures, and so essentially I machine learned the model, not statistical in that case, but actually the structural model of how people write texts.
• 37:01
  SPEAKER_04: And then I was again lucky, lucky, lucky.
• 37:04
  SPEAKER_04: I had all of these knowledge-based air battle simulators in the Air Force, and so I could go in and say, Hey, what happened in the battle?
• 37:13
  SPEAKER_04: And so I could actually run write a story about the battle, right?
• 37:17
  SPEAKER_04: I could then have an argument of was it a good battle or was it a bad battle?
• 37:20
  SPEAKER_04: Well, now I can make an argument based on all these events, right?
• 37:23
  SPEAKER_04: And if someone says, Hey, could you tell me describe that ingress and and bombing and egress run?
• 37:28
  SPEAKER_04: I could write a description.
• 37:30
  SPEAKER_04: Well, how did it differ from the last 10 runs that you've had?
• 37:34
  SPEAKER_04: This is the way I and then we actually had methods of similarity.
• 37:37
  SPEAKER_04: So I actually became a much better writer, both because you're writing a 350-page thesis and it's being critiqued by a bunch of smart people or good writers, uh, authors typically of text, but also your machine, I had machine my own machine, learned how to write, and then I taught my machine how to do that.
• 37:55
  SPEAKER_04: So today I would do it totally different.
• 37:57
  SPEAKER_04: Today I'd I'd get a GPT three, I'd I'd throw every single text in the world, right?
• 38:02
  SPEAKER_04: And I'd and I'd learn.
• 38:03
  SPEAKER_04: But the the the interesting thing was what I could add, which which a GPT couldn't add, is multiple levels of representation.
• 38:11
  SPEAKER_04: So syntax, the words and how they go together, semantics, what are the meaning of those words, uh, the actions, the the actors, and so on.
• 38:19
  SPEAKER_04: And then importantly, pragmatics, the intent.
• 38:22
  SPEAKER_04: Why is somebody writing that that are you know, that article or that statement?
• 38:27
  SPEAKER_04: Are they trying to convince you?
• 38:28
  SPEAKER_04: Are they trying to, you know, educate you?
• 38:31
  SPEAKER_04: Are they trying to confuse you, deceive you, right?
• 38:34
  SPEAKER_04: And so it understanding and actually representing what are called communicative acts, the actions that we take with words.
• 38:41
  SPEAKER_04: Why do we do things?
• 38:42
  SPEAKER_04: We do things sometimes to inspire people, sometimes we do things to deceive people.
• 38:46
  SPEAKER_04: And so, you know, having a machine understand that and then apply those methods also then allows it to recognize because all those were captured, by the way, but final point in plan operators.
• 38:57
  SPEAKER_04: So I use planning technology, I used human machine learning to represent, and I looked at the way that authors wrote for various purposes.
• 39:07
  SPEAKER_04: Um, and then I captured all of that in a computational paradigm, and uh, and they gave me a doctorate for it.
• 39:14
  SPEAKER_04: And I I I I loved it.
• 39:15
  SPEAKER_04: It was a lot of fun, it was great.
• 39:17
  SPEAKER_04: Um, and then I went off to have the rest of my life.
• 39:19
  SPEAKER_02: Well, I mean, Seth and I actually had a had an interview with um on a podcast, and uh his name is Henrik, and he was actually talking about synthetic media, which arguably you know, this starts to bleed into synthetic media, but more importantly, um, we should you know figure out how to canonize this into a product to go out to you know, because everything's true on the interwebs, right?
• 39:45
  SPEAKER_02: So let's take let's take your algorithm and go grade everybody's uh articles that are out there that are fixed that are made up or and and give people a score of no, this is truthful.
• 39:56
  SPEAKER_02: They're trying to educate, give an education, a confusing score, a you know, convincing score, whatever that is, and say, here's how these people are writing these structures.
• 40:07
  SPEAKER_02: They're just doing it.
• 40:08
  SPEAKER_02: You actually get the meta-meta of actually what's going on.
• 40:13
  SPEAKER_04: It's a great talk, Chris.
• 40:14
  SPEAKER_04: And I do think actually that as we get more sophisticated, you know, with adversarial AI, where we're able to confuse people with, you know, uh re refabricated speech or video images, uh, and of course, language, right?
• 40:28
  SPEAKER_04: How do we know, you know, true that something's truly attributed to Britain?
• 40:31
  SPEAKER_04: This is a this is a great challenge identity with with respect to information security.
• 40:36
  SPEAKER_04: Um, but it's also the case that it gives us, as you point out, a new tool, a new inspection tool.
• 40:42
  SPEAKER_04: And so interestingly, already today there are companies on Wall Street that actually will do sentiment analytics of things like investors.
• 40:49
  SPEAKER_04: So, what do people think about Stanley Black and Decker stock?
• 40:52
  SPEAKER_04: What do they think about, you know, Boeing stock?
• 40:54
  SPEAKER_04: What do they think about uh the Department of Defense, right?
• 40:57
  SPEAKER_04: Um, and and we can do this.
• 40:59
  SPEAKER_04: And by the way, I I was very, very fortunate uh about mid-career at MITRE.
• 41:03
  SPEAKER_04: I actually was the government hired me to run a program with a bunch of universities uh and some commercial companies to create what was then called the opinion bank, which is basically a collection of data, annotated data, um, regarding sentiment.
• 41:19
  SPEAKER_04: And so we it was the foundation for the whole field, which has become now known as sentiment analytics.
• 41:24
  SPEAKER_04: So the foundation for that was the government realizing, you know, this is well before, by the way, interestingly, you know, hate speech slash election uh, you know, uh, you know, uh, you know, kinds of influence campaigns, uh adversarial campaigns became an issue.
• 41:40
  SPEAKER_04: Um, that uh that uh that you know, we we had recognized there was going to be a need to detect sentiment, even just simply, simply like, you know, is a population happy or unhappy?
• 41:50
  SPEAKER_04: Um what are they unhappy about?
• 41:52
  SPEAKER_04: You know, so when you mix topic and detection together with sentiment analysis, and then to your point, if you can get to intent, you know, what is this person doing?
• 42:01
  SPEAKER_04: Are they clarifying something or are they, you know, are they using a simple simple thing?
• 42:05
  SPEAKER_04: Can you correlate that with facts and with with with authoritative sources?
• 42:10
  SPEAKER_04: And if it turns out you can't, well, you know, as we've seen in in lots of of uh of legal cases, you know, you know, a judge takes a look at something, the first thing they're looking at is what's the basis for this?
• 42:21
  SPEAKER_04: Where's the evidence?
• 42:21
  SPEAKER_04: And they they have a interestingly, the argumentation language that the legal community used, I codified in the argumentation uh uh strategies that I created.
• 42:31
  SPEAKER_04: Um, because the humans uh and you know, human legal reasoners actually use this language.
• 42:37
  SPEAKER_04: So they talk about, you know, what is the evidence?
• 42:40
  SPEAKER_04: What's the warrant that that that connects that evidence to some particular, you know, uh claim?
• 42:46
  SPEAKER_04: Uh and so you have to have basis for that.
• 42:47
  SPEAKER_04: So there's actually actually good human thinking that hopefully will get exposed and allow people to learn better and then maybe better manage some of these new challenges we face with our media.
• 42:58
  SPEAKER_02: I love it.
• 42:59
  SPEAKER_02: I love it.
• 43:00
  SPEAKER_02: Yeah, it's it's you know, it's like you said, it can actually bring a lot of clarity.
• 43:05
  SPEAKER_02: Sentiment is semantic, but um intent can actually then drive what the sent the semantic sentiment can mean.
• 43:13
  SPEAKER_02: So then you can actually drive what is really trying to, you know, what they're what is trying to be, you know, intended on on the outcome.
• 43:21
  SPEAKER_04: So that's right, Chris.
• 43:22
  SPEAKER_04: And and you know, one of the things I I teach, I've taught all my children, uh, they probably hate me for this, but um, I I I I forced them, I force them all at some point in their career, typically in in junior high school, but sometimes in high school, uh, to actually write an essay that specifically does an analysis or uses um what the what the rhetoricians were taught, uh, namely Socrates, uh, Plato and Aristotle, who, you know, uh used to teach, particularly uh, you know, the the uh the the Roman uh emperors, you know, how to make arguments.
• 43:53
  SPEAKER_04: And what they would say, they would talk about ethos, logos, and pathos.
• 43:56
  SPEAKER_04: So, you know, logos, make sure you got a logical argument, right?
• 43:59
  SPEAKER_04: Uh If if you're trying to tell me that the, you know, that the uh the the sky is uh is green and I can look up and see it's it's blue, well I'm probably not gonna believe you, right?
• 44:09
  SPEAKER_04: So make sure it's factual based.
• 44:10
  SPEAKER_04: So logos, you know, uh ethos, right?
• 44:13
  SPEAKER_04: Make sure your argument is ethically based.
• 44:16
  SPEAKER_04: If all my if my only purpose is to empty your pockets and it's not to consider your well-being, it's probably not that ethical, right?
• 44:23
  SPEAKER_04: Yeah.
• 44:24
  SPEAKER_04: My only basis is to get power and not to use it positively, right?
• 44:27
  SPEAKER_04: Uh, that's probably not an ethical uh argument you're gonna make or action you're gonna make.
• 44:31
  SPEAKER_04: And then finally, pathos.
• 44:33
  SPEAKER_04: Um, we know that humans are influenced by passion.
• 44:35
  SPEAKER_04: In fact, maybe too much in our day and age, you know, where we see, you know, a company comes on and advertises their product, and you're like, you watch it, and you know, there's a beautiful grandmother-granddaughter interaction, but it's like, what was that product really about?
• 44:48
  SPEAKER_04: And did I learn anything about that product?
• 44:51
  SPEAKER_04: So the point is you're gonna have all these things in balance, ethos, logos, and pathos, uh, if you actually are gonna be a good leader and a good communicator.
• 44:59
  SPEAKER_02: Yeah, at the end of those, I love at the end of those, some of those um medical, like can give you uncomfortable vomiting, hot dog fingers.
• 45:08
  SPEAKER_02: Beautiful people sitting there, and you're like, wait, what you know what I mean?
• 45:13
  SPEAKER_02: Shouldn't they be in a cemetery, not in a boat?
• 45:16
  SPEAKER_02: Yeah, like where why why are they not in a infirmary or somewhere, like you know, dealing with like IVs and I'm like, this is what this drug costs, but it does solve, you know, that you can have better eyesight.
• 45:28
  SPEAKER_02: There you go.
• 45:29
  SPEAKER_01: So what's going on what what's uh what's in your agenda moving forward?
• 45:33
  SPEAKER_01: So you have the new advisory role, you have this new potential executive role, you can't too talk too much about that.
• 45:38
  SPEAKER_01: When is that going to be announced, by the way?
• 45:40
  SPEAKER_04: Uh it should be should be a few weeks.
• 45:42
  SPEAKER_04: So it should be in the in the uh in the near term.
• 45:45
  SPEAKER_04: Um, you know, I think um what what I'm excited about is, and I've always I've learned about myself, is I love diversity.
• 45:53
  SPEAKER_04: I love diversity of thought, I love diversity of experiences, I love diversity of challenges.
• 45:58
  SPEAKER_04: So going forward, um, you know, I'm fortunate to be a special government uh uh employee and SGE for the Defense Science Board, running now a national study.
• 46:08
  SPEAKER_04: Uh that's exciting.
• 46:09
  SPEAKER_04: That really you what is that study about?
• 46:11
  SPEAKER_01: What is that study about?
• 46:12
  SPEAKER_04: That study is about uh uh digital engineering.
• 46:15
  SPEAKER_04: So so actually we're oh that's right.
• 46:16
  SPEAKER_01: Okay, what you had just mentioned before.
• 46:18
  SPEAKER_04: Yeah, what I mentioned before.
• 46:19
  SPEAKER_04: Yeah.
• 46:19
  SPEAKER_04: And and I'm just finishing up a classified study I can't talk about, although actually there's an unclassified summary of it called uh uh uh for for homeland air defense or H A D, uh the Defense Science Board, me and Mark Russell, the the CTO of Raytheon, just completed with a great team of really amazing folks.
• 46:37
  SPEAKER_04: Uh and so we've come up with a uh a strategy to uh to ensure America continues uh to be safe uh uh uh from from uh particular air threats, um, which are which are a particular concern.
• 46:50
  SPEAKER_04: And it's been it's been a wonderful experience.
• 46:53
  SPEAKER_04: Actually, the reason uh my my former company, MITRE, was created, was to create the semi-automated ground environment, Sage.
• 47:00
  SPEAKER_04: And uh Mark and I and the team proposed what we call Sage 2, which is it's like 80 years later or something.
• 47:06
  SPEAKER_04: This is the 50s, right?
• 47:07
  SPEAKER_04: So 70 years later, uh we, you know, it's it's time for a modern version, right?
• 47:12
  SPEAKER_04: And uh, and so um uh those are kinds of things uh to answer your question, Seth, that really excite me.
• 47:18
  SPEAKER_04: And and I then I do I do liking like working with um, I I have a um I'm I have a uh a video in production.
• 47:25
  SPEAKER_04: I'm working with a uh a Hollywood producer who normally does full full-length films and uh more more that for coming, just a little taste, a little preview, but but that's that's focusing on uh getting uh high school and college students excited about potential careers and artificial intelligence, especially working in the public interest.
• 47:44
  SPEAKER_04: So, you know, going into federal service or or you know, whether it be in Department of Defense, one of the national labs.
• 47:50
  SPEAKER_04: And that's really exciting because I'm I'm looking at all of the advanced applications, everything from you know, the fact that we've saved 10 pilots um uh already in F-16s from crashing by having an automated collision avoidance system to uh to you know systems that uh help uh us uh you know improve the situation awareness of commanders uh so that that their their troops are protected.
• 48:15
  SPEAKER_04: You know, it's a just a lot of a lot of exciting things in addition to the startups I mentioned.
• 48:20
  SPEAKER_04: So that that's that's kind of what's in my near future.
• 48:23
  SPEAKER_01: That's great.
• 48:24
  SPEAKER_01: And what makes you most excited about some of the startups?
• 48:27
  SPEAKER_01: Uh you you you mentioned the um Ready Robotics, and you you talked a little bit, maybe you can talk a little bit more about what what they're doing that's interesting.
• 48:34
  SPEAKER_04: Yeah, yeah, sure.
• 48:35
  SPEAKER_04: Um, you know, Ready Robotics is a fascinating company because um they uh uh they they really are, as I was sort of alluding to, uh they've developed an ability to uh uh through their Forge OS operating system, basically provide a common operating system across a multiplicity of robots.
• 48:52
  SPEAKER_04: So instead of, you know, if you've got 20 different robots operating, you know, we had 108 plants around the world at Stanley Black and Decker.
• 48:59
  SPEAKER_04: And you can just imagine all the kinds of robots that we had from just about every manufacturer.
• 49:04
  SPEAKER_04: Um, well, the problem is, you know, if you train one person on one robot, well, you've trained one person on one robot.
• 49:10
  SPEAKER_04: And so instead, what you want to have is you want to have an intuitive interface to be able to control not only one robot, but actually we talk about um, you know, human to robot uh uh ratios, like, you know, can I control two robots or four robots or 10 robots?
• 49:25
  SPEAKER_04: And and so uh that that that's you know one of the things that excites me uh about uh about Ready Robotics.
• 49:32
  SPEAKER_04: Um I think you know, uh, you know, uh Floral Technologies is an interesting company.
• 49:37
  SPEAKER_04: Um they actually have a uh have a series of uh antimicrobial coatings.
• 49:42
  SPEAKER_04: So these are nanotechnologies that have been proven out in federal labs that can basically get rid of uh bugs or they can uh actually be uh be um uh hydrophobic so they can you know keep uh water and freezing, let's say, like off of a wing.
• 49:57
  SPEAKER_04: Um also anti-corrosive, you know, you can paint them on a ship uh and either either increase the laminar flow of that ship, reducing energy consumption, and or decrease you know the barnacles on the ship, the corrosion ship, and so on.
• 50:08
  SPEAKER_04: So some exciting uh things that that you know technology uh enables uh is is is uh sort of something that continues to get me excited.
• 50:18
  SPEAKER_04: You know, we we have we have had a whole bunch of companies um we invested in at uh at our venture capital, our venture capital arm.
• 50:25
  SPEAKER_04: We invested in 43 companies, three of them went bankrupt, three of them we acquired.
• 50:30
  SPEAKER_04: Uh the other ones did really well.
• 50:32
  SPEAKER_04: We we we make money on those, but more importantly, we get a strategic advantage to investing in those early companies.
• 50:39
  SPEAKER_04: You know, we get advantageous in terms of 3D printing technology, digital analytics technology, sensor processing technology, all of these can really be differential in in commercialization.
• 50:50
  SPEAKER_01: And and you know, we didn't we just mentioned it, but maybe you could just give I know we're almost out of time here, but uh the the COVID deactivate uh deactivization system that was pretty cool, Seth.
• 51:02
  SPEAKER_04: It's a it's a great question.
• 51:04
  SPEAKER_04: So um about you can imagine in the middle of COVID, um uh we uh I get a call from my CEO, or it's just really the beginning of COVID.
• 51:12
  SPEAKER_04: And since we were global, we had operations in China.
• 51:14
  SPEAKER_04: So in the very beginning of 2020, January 20, 2020, we knew we had a problem.
• 51:18
  SPEAKER_04: Uh, because you know, we it was disrupting our, you know, we workers weren't coming back from the Chinese you know holiday and so on, and uh plants were getting shut down.
• 51:26
  SPEAKER_04: So we knew we had a problem immediately.
• 51:27
  SPEAKER_04: So my boss called me up and said, I got a call from an old col GE colleague of mine.
• 51:33
  SPEAKER_04: Um uh he has got an idea about how we could deactivate COVID.
• 51:36
  SPEAKER_04: And I and I said, Oh, that's crazy, that's gonna be impossible, it's not possible, and so on.
• 51:40
  SPEAKER_04: Well, in fact, I started to uh uh dig in to this and I discovered and you know, you know, started just learning that, well, you know, actually bacteria are living, but actually COVID isn't a isn't a living, uh, it's it's just a non-living molecule.
• 51:53
  SPEAKER_04: The SARS Co is a virus.
• 51:55
  SPEAKER_04: But what I what I started doing is doing some research and discovered in the literature you could deactivate it with heat, except it took a lot of time, like 30 minutes.
• 52:04
  SPEAKER_04: And so there were some colleagues uh of mine, um uh uh the uh CEO of Battel, I was chatting with uh DSP colleague of mine, and um he was telling me uh we've invented this new um vaporization uh of of uh of hydrogen peroxide that actually can decontaminate masks.
• 52:23
  SPEAKER_04: And that's great, but you know, you gotta have you know big containers and you gotta you know put stuff in the container store.
• 52:29
  SPEAKER_04: So I said, what could we do?
• 52:30
  SPEAKER_04: And basically we discovered a combination of um uh the we looked at the nature of SARS-CoV-2 and a combination we don't realize it was electrostatically charged.
• 52:41
  SPEAKER_04: So a combination of electrostatic attraction, high heat, a little, and then this was this was the the the invention, the sinuous design.
• 52:50
  SPEAKER_04: We invented uh and then tested in a national laboratory at MIT Lincoln Laboratory at a biosafety level tool facility in the middle of COVID, right?
• 53:00
  SPEAKER_04: And this was all of us were were working you know remotely and at night, you know, because this was kind of a special project.
• 53:06
  SPEAKER_04: But we basically invented a mask, a portable air respirator, and a room uh air purifier, and showed that we could deactivate essentially immediately within seconds, we could deactivate the bug.
• 53:19
  SPEAKER_04: Now, unf maybe fortunately, the COVID, the SARS SARS-CoV-2 danger, you know, along came the vaccines, the market kind of dissipated.
• 53:27
  SPEAKER_04: Right.
• 53:28
  SPEAKER_04: And it's a great lesson in commercialization how you got to get stuff to market fast, but it's on the shelf, and we've got patents and and we got capabilities.
• 53:36
  SPEAKER_04: So, you know, should we need that in the future, we can manufacture that.
• 53:39
  SPEAKER_04: And so that was an exciting experience.
• 53:41
  SPEAKER_04: Yeah, interesting.
• 53:42
  SPEAKER_01: That's wonderful innovation.
• 53:44
  SPEAKER_01: Well, listen, it has been a real pleasure to speak with you today.
• 53:48
  SPEAKER_01: Thank you so much for your time.
• 53:50
  SPEAKER_01: Uh really enjoyed it.
• 53:52
  SPEAKER_04: My pleasure, Seth and Chris.
• 53:53
  SPEAKER_04: Uh, the best, best of both of you.
• 53:55
  SPEAKER_04: And uh um wish all your listeners to be well and be safe and uh prosper.
• 54:01
  SPEAKER_02: If if if you know when when folks listen to it, they're gonna be, I think, blown away by two things.
• 54:06
  SPEAKER_02: One, um your ability to handle um quite a bit of just you know, of activities, right?
• 54:14
  SPEAKER_02: That's probably your scheduling mind.
• 54:16
  SPEAKER_02: But I think the other the other thing that's probably more important is just your passion for life and passion for things that you're you know that are that you want to get you know get into and drive, right?
• 54:27
  SPEAKER_02: And that's palpable.
• 54:28
  SPEAKER_02: So thanks for the energy and everything else you brought.
• 54:32
  SPEAKER_01: And we'll have to have you to our next knowledge salon.
• 54:35
  SPEAKER_01: Uh we hope hope to do another one before the end of the year.
• 54:37
  SPEAKER_01: So uh maybe we can help you out for that.
• 54:40
  SPEAKER_04: Happy to do so.
• 54:41
  SPEAKER_04: Wonderful to see you both.
• 54:46
  SPEAKER_00: Thanks for listening to the Early AI podcast with your hosts, Seth Early and Chris Featherstone.
• 54:52
  SPEAKER_00: We hope you enjoyed the program and took away some ideas that you can start to implement in your organization right now.
• 54:58
  SPEAKER_00: If you still need help, check out www.early.com for information about early information today and of course you can check the show notes for links to any resources we mentioned today.